AI0-001 AI Infrastructure and Technologies Practice Question
A team is deploying a machine learning model on a Kubernetes cluster. They need to ensure low-latency inference and efficient resource utilization. Which approach should they use to dynamically scale inference pods based on request volume?
⚠ Common exam trap
A common misconception is that batch processing (Jobs) or static scaling is suitable for real-time inference, when in fact dynamic scaling with HPA is required to balance latency and resource efficiency in Kubernetes.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a Horizontal Pod Autoscaler (HPA) with target CPU utilization
The Horizontal Pod Autoscaler (HPA) is the correct choice because it automatically scales the number of inference pods based on observed CPU utilization or custom metrics, ensuring low-latency inference by adding replicas during traffic spikes and reducing waste during idle periods. This dynamic scaling aligns with the need for efficient resource utilization in a Kubernetes cluster, as it adjusts pod count in real-time to match request volume without manual intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a Job resource to process requests in batch
Why it's wrong here
A Job runs pods to completion for batch workloads and does not scale replicas in response to live request volume. Horizontal Pod Autoscaler is the mechanism that adjusts replica counts from metrics; Jobs suit offline processing, not low-latency inference.
- ✗
Deploy a single large pod on a powerful node
Why it's wrong here
One large pod on a single node cannot scale replica count with request volume and concentrates load on one node, so it fails the dynamic scaling requirement. Horizontal Pod Autoscaler is needed; a single large pod suits steady, predictable workloads.
- ✓
Use a Horizontal Pod Autoscaler (HPA) with target CPU utilization
Why this is correct
Horizontal Pod Autoscaler adjusts replica counts from observed CPU utilisation, satisfying the low-latency and efficient-resource constraint by matching pod capacity to request-driven load. It scales horizontally within the cluster, so inference pods expand as traffic rises and contract when idle, avoiding the over-provisioning that fixed replicas would cause.
- ✗
Set a fixed number of pod replicas equal to the maximum expected load
Why it's wrong here
Fixed replicas sized to peak load never adjust to actual request volume, so capacity sits idle off-peak and cannot grow beyond the set count. Horizontal Pod Autoscaler varies replicas from observed metrics; fixed replicas suit stable, unchanging demand.
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